Thanks. What if I use batch calculation instead of stream computing? Do I still need that much memory? For example, if the 24 hour data set is 100 GB. Do I also need a 100GB RAM to do the one time batch calculation ?
At 2016-05-09 15:14:47, "Saisai Shao" <sai.sai.s...@gmail.com> wrote: For window related operators, Spark Streaming will cache the data into memory within this window, in your case your window size is up to 24 hours, which means data has to be in Executor's memory for more than 1 day, this may introduce several problems when memory is not enough. On Mon, May 9, 2016 at 3:01 PM, Mich Talebzadeh <mich.talebza...@gmail.com> wrote: ok terms for Spark Streaming "Batch interval" is the basic interval at which the system with receive the data in batches. This is the interval set when creating a StreamingContext. For example, if you set the batch interval as 300 seconds, then any input DStream will generate RDDs of received data at 300 seconds intervals. A window operator is defined by two parameters - - WindowDuration / WindowsLength - the length of the window - SlideDuration / SlidingInterval - the interval at which the window will slide or move forward Ok so your batch interval is 5 minutes. That is the rate messages are coming in from the source. Then you have these two params // window length - The duration of the window below that must be multiple of batch interval n in = > StreamingContext(sparkConf, Seconds(n)) val windowLength = x = m * n // sliding interval - The interval at which the window operation is performed in other words data is collected within this "previous interval' val slidingInterval = y l x/y = even number Both the window length and the slidingInterval duration must be multiples of the batch interval, as received data is divided into batches of duration "batch interval". If you want to collect 1 hour data then windowLength = 12 * 5 * 60 seconds If you want to collect 24 hour data then windowLength = 24 * 12 * 5 * 60 You sliding window should be set to batch interval = 5 * 60 seconds. In other words that where the aggregates and summaries come for your report. What is your data source here? HTH Dr Mich Talebzadeh LinkedIn https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw http://talebzadehmich.wordpress.com On 9 May 2016 at 04:19, kramer2...@126.com<kramer2...@126.com> wrote: We have some stream data need to be calculated and considering use spark stream to do it. We need to generate three kinds of reports. The reports are based on 1. The last 5 minutes data 2. The last 1 hour data 3. The last 24 hour data The frequency of reports is 5 minutes. After reading the docs, the most obvious way to solve this seems to set up a spark stream with 5 minutes interval and two window which are 1 hour and 1 day. But I am worrying that if the window is too big for one day and one hour. I do not have much experience on spark stream, so what is the window length in your environment? Any official docs talking about this? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/How-big-the-spark-stream-window-could-be-tp26899.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
